Abstract
In this paper, we introduce StreakNet-Arch, a novel signal processing architecture designed for Underwater Carrier LiDAR-Radar (UCLR) imaging systems, to address the limitations in scatter suppression and real-time imaging. StreakNet-Arch formulates the signal processing as a real-time, end-to-end binary classification task, enabling real-time image acquisition. To achieve this, we leverage Self-Attention networks and propose a novel Double Branch Cross Attention (DBC-Attention) mechanism that surpasses the performance of traditional methods. Furthermore, we present a method for embedding streak-tube camera images into attention networks, effectively acting as a learned bandpass filter. To facilitate further research, we contribute a publicly available streak-tube camera image dataset. The dataset contains 2,695,168 real-world underwater 3D point cloud data. These advancements significantly improve UCLR capabilities, enhancing its performance and applicability in underwater imaging tasks. The source code and dataset can be found at this https URL .
Abstract (translated)
在本文中，我们提出了StreakNet-Arch，一种专为水下载体激光雷达（UCLR）成像系统设计的信号处理架构，以解决散射抑制和实时成像的局限性。StreakNet-Arch将信号处理表示为实时、端到端的二分类任务，实现实时图像采集。为了实现这一目标，我们利用自注意力网络并提出了一种新颖的双分支交叉注意（DBC-Attention）机制，超越了传统方法的性能。此外，我们还提出了一种将轨迹管相机图像嵌入注意力网络的方法，有效地充当了一个学习带通滤波器。为了促进进一步的研究，我们贡献了一个公开可用的轨迹管相机图像数据集。该数据集包含2,695,168个真实世界的水下3D点云数据。这些进步显著提高了UCLR的功能，提高了其在水下成像任务中的性能和适用性。源代码和数据集可以在这个链接中找到。
URL
https://arxiv.org/abs/2404.09158
